import numpy as np
import pandas as pd
from keras.utils import np_utils
from keras.datasets import mnist
from function import plot_images_labels_prediction

np.random.seed(10)

(x_train_image, y_train_label), (x_test_image, y_test_label) = mnist.load_data()

# plot_image(x_train_image[0])

# plot_images_labels_prediction(x_test_image, y_test_label, [], 0, 10)

x_Train = x_train_image.reshape(60000, 784).astype('float32')
x_Test = x_test_image.reshape(10000, 784).astype('float32')

x_Train_normalize = x_Train / 255
x_Test_normalize = x_Test / 255

y_TrainOneHot = np_utils.to_categorical(y_train_label)
y_TestOneHot = np_utils.to_categorical(y_test_label)
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